SMOP is Small Matlab and Octave to Python compiler.
SMOP translates matlab to python. Despite obvious similarities
between matlab and numeric python, there are enough differences to
make manual translation infeasible in real life. SMOP generates
human-readable python, which also appears to be faster than octave.
Just how fast? Timing results for “Moving furniture” are shown
in Table 1. It seems that for this program, translation to python
resulted in about two times speedup, and additional two times speedup
was achieved by compiling SMOP run-time library
to C, using cython. This pseudo-benchmark measures scalar
performance, and my interpretation is that scalar computations are
of less interest to the octave team.
octave-3.8.1 190 ms
smop+python-2.7 80 ms
smop+python-2.7+cython-0.20.1 40 ms
Table 1. SMOP performance  


October 15, 2014
Version 0.26.3 is available for beta testing.
Next version 0.27 is planned to compile octave
scripts library, which contains over 120 KLOC in
almost 1,000 matlab files. There are 13 compilation
errors with smop 0.26.3 .


  • Network installation is the best method if you just want it to
    run the example:

    $ easy_install smop --user
  • Install from the sources if you are behind a firewall:

    $ tar zxvf smop.tar.gz
    $ cd smop
    $ python install --user
  • Fork github repository if you need the latest fixes.

  • Finally, it is possible to use smop without doing the installation,
    but only if you already installed the dependences — numpy
    and networkx:

    $ tar zxvf smop.tar.gz
    $ cd smop/smop
    $ python solver.m
    $ python

Working example

We will translate solver.m to present a sample of smop features. The
program was borrowed from the matlab programming competition in 2004 (Moving
Furniture).To the left is solver.m. To the right is — its
translation to python. Though only 30 lines long, this
example shows many of the complexities of converting matlab code
to python.

01   function mv = solver(ai,af,w)  01 def solver_(ai,af,w,nargout=1):
02   nBlocks = max(ai(:));          02     nBlocks=max_(ai[:])
03   [m,n] = size(ai);              03     m,n=size_(ai,nargout=2)
02 Matlab uses round brackets both for array indexing and
for function calls. To figure out which is which,
SMOP computes local use-def information, and then
applies the following rule: undefined names are
functions, while defined are arrays.
03 Matlab function size returns variable number of
return values, which corresponds to returning a tuple
in python. Since python functions are unaware of the
expected number of return values, their number must be
explicitly passed in nargout.

04   I = [0  1  0 -1];              04     I=matlabarray([0,1,0,- 1])
05   J = [1  0 -1  0];              05     J=matlabarray([1,0,- 1,0])
06   a = ai;                        06     a=copy_(ai)
07   mv = [];                       07     mv=matlabarray([])
04 Matlab array indexing starts with one; python indexing
starts with zero. New class matlabarray derives from
ndarray, but exposes matlab array behaviour. For
example, matlabarray instances always have at least
two dimensions — the shape of I and J is [1 4].
06 Matlab array assignment implies copying; python
assignment implies data sharing. We use explicit copy
07 Empty matlabarray object is created, and then
extended at line 28. Extending arrays by
out-of-bounds assignment is deprecated in matlab, but
is widely used never the less. Python ndarray
can’t be resized except in some special cases.
Instances of matlabarray can be resized except
where it is too expensive.

08   while ~isequal(af,a)           08     while not isequal_(af,a):
09     bid = ceil(rand*nBlocks);    09         bid=ceil_(rand_() * nBlocks)
10     [i,j] = find(a==bid);        10         i,j=find_(a == bid,nargout=2)
11     r = ceil(rand*4);            11         r=ceil_(rand_() * 4)
12     ni = i + I(r);               12         ni=i + I[r]
13     nj = j + J(r);               13         nj=j + J[r]
09 Matlab functions of zero arguments, such as
rand, can be used without parentheses. In python,
parentheses are required. To detect such cases, used
but undefined variables are assumed to be functions.
10 The expected number of return values from the matlab
function find is explicitly passed in nargout.
12 Variables I and J contain instances of the new class
matlabarray, which among other features uses one
based array indexing.

14     if (ni<1) || (ni>m) ||       14         if (ni < 1) or (ni > m) or
               (nj<1) || (nj>n)                            (nj < 1) or (nj > n):
15         continue                 15             continue
16     end                          16
17     if a(ni,nj)>0                17         if a[ni,nj] > 0:
18         continue                 18           continue
19     end                          19
20     [ti,tj] = find(af==bid);     20         ti,tj=find_(af == bid,nargout=2)
21     d = (ti-i)^2 + (tj-j)^2;     21         d=(ti - i) ** 2 + (tj - j) ** 2
22     dn = (ti-ni)^2 + (tj-nj)^2;  22         dn=(ti - ni) ** 2 + (tj - nj) ** 2
23     if (d<dn) && (rand>0.05)     23         if (d < dn) and (rand_() > 0.05):
24         continue                 24             continue
25     end                          25
26     a(ni,nj) = bid;              26         a[ni,nj]=bid
27     a(i,j) = 0;                  27         a[i,j]=0
28     mv(end+1,[1 2]) = [bid r];   28         mv[mv.shape[0] + 1,[1,2]]=[bid,r]
29  end                             29
30                                  30     return mv

Implementation status

Random remarks

With less than five thousands lines of python code
SMOP does not pretend to compete with such polished
products as matlab or octave. Yet, it is not a toy.
There is an attempt to follow the original matlab
semantics as close as possible. Matlab language
definition (never published afaik) is full of dark
corners, and SMOP tries to follow matlab as
precisely as possible.
There is a price, too.
The generated sources are
matlabic, rather than pythonic, which means that
library maintainers must be fluent in both languages,
and the old development environment must be kept around.
Should the generated program be pythonic or matlabic?

For example should array indexing start with zero
(pythonic) or with one (matlabic)?

I beleive now that some matlabic accent is unavoidable
in the generated python sources. Imagine matlab program
is using regular expressions, matlab style. We are not
going to translate them to python style, and that code
will remain forever as a reminder of the program’s
matlab origin.

Another example. Matlab code opens a file; fopen
returns -1 on error. Pythonic code would raise
exception, but we are not going to do that. Instead,
we will live with the accent, and smop takes this to the
extreme — the matlab program remains mostly unchanged.

It turns out that generating matlabic` allows for
moving much of the project complexity out of the
compiler (which is already complicated enough) and into
the runtime library, where there is almost no
interaction between the library parts.

Which one is faster — python or octave? I don’t know.
Doing reliable performance measurements is notoriously
hard, and is of low priority for me now. Instead, I wrote
a simple driver go.m and and rewrote rand
so that python and octave versions run the same code.
Then I ran the above example on my laptop. The results
are twice as fast for the python version. What does it
mean? Probably nothing. YMMV.

ai = zeros(10,10);
af = ai;



mv = solver(ai,af,0);

Running the test suite:

$ cd smop
$ make check
$ make test

Command-line options

lei@dilbert ~/smop-github/smop $ python -h
SMOP compiler version 0.25.1
Usage: smop [options] file-list
    -V --version
    -X --exclude=FILES      Ignore files listed in comma-separated list FILES
    -d --dot=REGEX          For functions whose names match REGEX, save debugging
                            information in "dot" format (see
                            You need an installation of graphviz to use --dot
                            option.  Use "dot" utility to create a pdf file.
                            For example:
                                $ python fastsolver.m -d "solver|cbest"
                                $ dot -Tpdf -o resolve_solver.pdf
    -h --help
    -o --output=FILENAME    By default create file named
    -o- --output=-          Use standard output
    -s --strict             Stop on the first error
    -v --verbose


View Github